Nigerian-TCGA Differential Expression Analysis Using Limma/Voom Protein-Coding Genes Only for whole-genome samples (PAM50) without ribosomal proteins
Padma Sheila Rajagopal, MD MPH
6/7/2020
Last updated: 2020-06-07
Checks: 6 1
Knit directory: ~/Research-Local/2019-rnaseq/TCGA-Nigerian-RNAseq/
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#Translation from HTSeq raw counts -> Count Matrix I have 84 TCGA patients with whole-genome sequencing data and RNAseq data as well as 96 Nigerian patients with RNA-seq data. Raw counts were initially processed using HTSeq, so HTSeq data is being formatted for use with DESeq2 and limma-voom.
sampleConditionPAM50
sampleConditionrace Basal Her2 LumA LumB Normal PAM_other
Nigerian 41 27 14 11 3 0
TCGA_black 23 0 4 4 0 0
TCGA_other 0 0 0 0 0 14
TCGA_white 17 5 8 9 0 0
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
#Quantile normalization Please refer to: https://parajago.github.io/TCGA-Nigerian-RNAseq/NigerianTCGArawcountsDeSeq2-pc2.html regarding comparison between the Nigerian and TCGA data sets and why quantile normalization under the limma-voom approach was chosen for primary differential expression analysis.
##Data visualization
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
In the raw data with log transformation only, we are able to see that there are two peaks corresponding to the two datasets (Nigerian and TCGA). The quantile normalization demonstrates a PCA that has similar clustering. Only ~20% of the distribution of the data set is explained by the PCA1, 2 of the variables.
#Bias in distribution of RNA counts across groups 
Spearman's rank correlation rho
data: jointcounts$mean.x and jointcounts$mean.y
S = 4.1134e+10, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.9678362
Spearman's rank correlation rho
data: jointcounts$sum.x and jointcounts$sum.y
S = 4.1134e+10, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.9678362
Call:
glm(formula = meancounts ~ condition1 + condition2 + condition1 *
condition2, data = outcomecounts_f)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.156662 -0.008532 0.006351 0.013649 0.052133
Coefficients: (3 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.205609 0.004132 1501.670 < 2e-16
condition1TCGA_black -0.047048 0.006893 -6.825 1.90e-10
condition1TCGA_white -0.044925 0.007633 -5.886 2.39e-08
condition2Her2 0.001799 0.006558 0.274 0.784
condition2LumA 0.005765 0.008191 0.704 0.483
condition2LumB 0.002947 0.008985 0.328 0.743
condition2Normal -0.015785 0.015826 -0.997 0.320
condition1TCGA_black:condition2Her2 NA NA NA NA
condition1TCGA_white:condition2Her2 -0.013656 0.014974 -0.912 0.363
condition1TCGA_black:condition2LumA 0.005072 0.016510 0.307 0.759
condition1TCGA_white:condition2LumA -0.018886 0.013993 -1.350 0.179
condition1TCGA_black:condition2LumB -0.012493 0.016918 -0.738 0.461
condition1TCGA_white:condition2LumB -0.019039 0.014132 -1.347 0.180
condition1TCGA_black:condition2Normal NA NA NA NA
condition1TCGA_white:condition2Normal NA NA NA NA
(Intercept) ***
condition1TCGA_black ***
condition1TCGA_white ***
condition2Her2
condition2LumA
condition2LumB
condition2Normal
condition1TCGA_black:condition2Her2
condition1TCGA_white:condition2Her2
condition1TCGA_black:condition2LumA
condition1TCGA_white:condition2LumA
condition1TCGA_black:condition2LumB
condition1TCGA_white:condition2LumB
condition1TCGA_black:condition2Normal
condition1TCGA_white:condition2Normal
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.0007001706)
Null deviance: 0.21944 on 165 degrees of freedom
Residual deviance: 0.10783 on 154 degrees of freedom
AIC: -721.22
Number of Fisher Scoring iterations: 2
Call:
glm(formula = meancounts ~ condition1 + binarybasal + condition1 *
binarybasal, data = outcomecounts_f)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.156662 -0.009006 0.005997 0.013868 0.053060
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.207688 0.003534 1756.730 < 2e-16 ***
condition1TCGA_black -0.048482 0.009916 -4.889 2.45e-06 ***
condition1TCGA_white -0.061053 0.006611 -9.235 < 2e-16 ***
binarybasal -0.002079 0.005407 -0.384 0.701
condition1TCGA_black:binarybasal 0.001434 0.012039 0.119 0.905
condition1TCGA_white:binarybasal 0.016128 0.010043 1.606 0.110
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.0006867717)
Null deviance: 0.21944 on 165 degrees of freedom
Residual deviance: 0.10988 on 160 degrees of freedom
AIC: -730.09
Number of Fisher Scoring iterations: 2
Call:
glm(formula = varcounts ~ condition1 + condition2 + condition1 *
condition2, data = outcomecounts_f)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.53187 -0.15632 -0.05508 0.10684 1.39968
Coefficients: (3 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.191178 0.040119 378.648 < 2e-16 ***
condition1TCGA_black 0.517159 0.066924 7.728 1.32e-12 ***
condition1TCGA_white 0.495656 0.074105 6.689 3.92e-10 ***
condition2Her2 -0.009674 0.063669 -0.152 0.879
condition2LumA -0.054497 0.079519 -0.685 0.494
condition2LumB -0.023179 0.087229 -0.266 0.791
condition2Normal 0.187594 0.153646 1.221 0.224
condition1TCGA_black:condition2Her2 NA NA NA NA
condition1TCGA_white:condition2Her2 0.134690 0.145376 0.926 0.356
condition1TCGA_black:condition2LumA -0.058155 0.160283 -0.363 0.717
condition1TCGA_white:condition2LumA 0.170866 0.135847 1.258 0.210
condition1TCGA_black:condition2LumB 0.113024 0.164244 0.688 0.492
condition1TCGA_white:condition2LumB 0.157791 0.137198 1.150 0.252
condition1TCGA_black:condition2Normal NA NA NA NA
condition1TCGA_white:condition2Normal NA NA NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.0659925)
Null deviance: 22.881 on 165 degrees of freedom
Residual deviance: 10.163 on 154 degrees of freedom
AIC: 33.408
Number of Fisher Scoring iterations: 2
Call:
glm(formula = varcounts ~ condition1 + binarybasal + condition1 *
binarybasal, data = outcomecounts_f)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.54130 -0.15692 -0.05639 0.11345 1.39968
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.17815 0.03436 441.672 < 2e-16 ***
condition1TCGA_black 0.51878 0.09644 5.379 2.61e-07 ***
condition1TCGA_white 0.63448 0.06429 9.869 < 2e-16 ***
binarybasal 0.01302 0.05258 0.248 0.805
condition1TCGA_black:binarybasal -0.00162 0.11708 -0.014 0.989
condition1TCGA_white:binarybasal -0.13882 0.09767 -1.421 0.157
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 0.0649533)
Null deviance: 22.881 on 165 degrees of freedom
Residual deviance: 10.393 on 160 degrees of freedom
AIC: 25.118
Number of Fisher Scoring iterations: 2
Call:
glm(formula = sumcounts ~ condition1 + condition2 + condition1 *
condition2, data = outcomecounts_f)
Deviance Residuals:
Min 1Q Median 3Q Max
-3090.0 -168.3 125.3 269.2 1028.3
Coefficients: (3 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 122399.42 81.51 1501.670 < 2e-16
condition1TCGA_black -927.98 135.97 -6.825 1.90e-10
condition1TCGA_white -886.10 150.55 -5.886 2.39e-08
condition2Her2 35.48 129.35 0.274 0.784
condition2LumA 113.70 161.56 0.704 0.483
condition2LumB 58.13 177.22 0.328 0.743
condition2Normal -311.34 312.16 -0.997 0.320
condition1TCGA_black:condition2Her2 NA NA NA NA
condition1TCGA_white:condition2Her2 -269.35 295.35 -0.912 0.363
condition1TCGA_black:condition2LumA 100.03 325.64 0.307 0.759
condition1TCGA_white:condition2LumA -372.51 275.99 -1.350 0.179
condition1TCGA_black:condition2LumB -246.40 333.69 -0.738 0.461
condition1TCGA_white:condition2LumB -375.52 278.74 -1.347 0.180
condition1TCGA_black:condition2Normal NA NA NA NA
condition1TCGA_white:condition2Normal NA NA NA NA
(Intercept) ***
condition1TCGA_black ***
condition1TCGA_white ***
condition2Her2
condition2LumA
condition2LumB
condition2Normal
condition1TCGA_black:condition2Her2
condition1TCGA_white:condition2Her2
condition1TCGA_black:condition2LumA
condition1TCGA_white:condition2LumA
condition1TCGA_black:condition2LumB
condition1TCGA_white:condition2LumB
condition1TCGA_black:condition2Normal
condition1TCGA_white:condition2Normal
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 272391.7)
Null deviance: 85369567 on 165 degrees of freedom
Residual deviance: 41948320 on 154 degrees of freedom
AIC: 2562.1
Number of Fisher Scoring iterations: 2
Call:
glm(formula = sumcounts ~ condition1 + binarybasal + condition1 *
binarybasal, data = outcomecounts_f)
Deviance Residuals:
Min 1Q Median 3Q Max
-3090.0 -177.6 118.3 273.5 1046.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 122440.43 69.70 1756.730 < 2e-16 ***
condition1TCGA_black -956.26 195.59 -4.889 2.45e-06 ***
condition1TCGA_white -1204.21 130.39 -9.235 < 2e-16 ***
binarybasal -41.01 106.65 -0.384 0.701
condition1TCGA_black:binarybasal 28.27 237.46 0.119 0.905
condition1TCGA_white:binarybasal 318.11 198.08 1.606 0.110
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 267179)
Null deviance: 85369567 on 165 degrees of freedom
Residual deviance: 42748643 on 160 degrees of freedom
AIC: 2553.3
Number of Fisher Scoring iterations: 2
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
The high rho and significant p-value in the Spearman rank correlation suggests that there is a strong correlation when comparing the means and sums across genes from the Nigerian and TCGA pools. We did not find any subtype specific interaction effects arising within the basal tumors (largest pool)
#Specific gene comparison 
This section is to compare the distribution/variance in gene counts for specific genes of breast cancer interest across both populations.
#Differential expression setup
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA White - Basal
[1] 14785 58
TCGA_white.Basal - Nigerian.Basal
Down 2344
NotSig 9126
Up 3300
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA Black - Basal
[1] 14864 64
TCGA_black.Basal - Nigerian.Basal
Down 2571
NotSig 9017
Up 3261
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##EGO/KEGG Pathway analysis: Basal
[1] 14785 58
TCGA_white.Basal - Nigerian.Basal
Down 2344
NotSig 9126
Up 3300
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA White - HER2 (no TCGA Black HER2+ patients)
[1] 13869 32
TCGA_white.Her2 - Nigerian.Her2
Down 311
NotSig 12912
Up 631
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##EGO/KEGG Pathway analysis: Her2
[1] 13869 32
TCGA_white.Her2 - Nigerian.Her2
Down 311
NotSig 12912
Up 631
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA White - LumA
[1] 13663 22
TCGA_white.LumA - Nigerian.LumA
Down 1086
NotSig 10938
Up 1624
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA Black - LumA
[1] 13530 18
TCGA_black.LumA - Nigerian.LumA
Down 213
NotSig 13046
Up 256
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##EGO/KEGG Pathway analysis: LumA
[1] 13663 22
TCGA_white.LumA - Nigerian.LumA
Down 1086
NotSig 10938
Up 1624
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA White - LumB
[1] 13082 20
TCGA_white.LumB - Nigerian.LumB
Down 980
NotSig 10975
Up 1112
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA Black - LumB
[1] 12993 15
TCGA_black.LumB - Nigerian.LumB
Down 94
NotSig 12767
Up 117
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##EGO/KEGG Pathway analysis: LumB
[1] 13082 20
TCGA_white.LumB - Nigerian.LumB
Down 980
NotSig 10975
Up 1112
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] msigdbr_7.1.1 fgsea_1.10.0
[3] Rcpp_1.0.1 AnnotationHub_2.16.1
[5] BiocFileCache_1.8.0 dbplyr_1.4.2
[7] Glimma_1.12.0 RColorBrewer_1.1-2
[9] preprocessCore_1.46.0 ashr_2.2-32
[11] ggfortify_0.4.7 calibrate_1.7.2
[13] MASS_7.3-51.5 sva_3.32.1
[15] mgcv_1.8-31 nlme_3.1-144
[17] EnsDb.Hsapiens.v75_2.99.0 ensembldb_2.8.0
[19] AnnotationFilter_1.8.0 GenomicFeatures_1.36.4
[21] hexbin_1.27.3 stringi_1.4.3
[23] dplyr_0.8.3 affy_1.62.0
[25] checkmate_1.9.3 pathview_1.24.0
[27] org.Hs.eg.db_3.8.2 AnnotationDbi_1.46.0
[29] clusterProfiler_3.12.0 pheatmap_1.0.12
[31] genefilter_1.66.0 vsn_3.52.0
[33] RUVSeq_1.18.0 EDASeq_2.18.0
[35] ShortRead_1.42.0 GenomicAlignments_1.20.0
[37] Rsamtools_2.0.0 Biostrings_2.52.0
[39] XVector_0.24.0 DESeq2_1.24.0
[41] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
[43] BiocParallel_1.18.0 matrixStats_0.54.0
[45] Biobase_2.44.0 GenomicRanges_1.36.0
[47] GenomeInfoDb_1.20.0 IRanges_2.18.1
[49] S4Vectors_0.22.0 BiocGenerics_0.30.0
[51] edgeR_3.26.4 limma_3.40.2
[53] ggbiplot_0.55 scales_1.0.0
[55] plyr_1.8.5 ggplot2_3.2.1
[57] gplots_3.0.3
loaded via a namespace (and not attached):
[1] rappdirs_0.3.1 rtracklayer_1.44.0
[3] R.methodsS3_1.7.1 tidyr_1.0.0
[5] acepack_1.4.1 bit64_0.9-7
[7] knitr_1.28 aroma.light_3.14.0
[9] R.utils_2.8.0 data.table_1.12.8
[11] rpart_4.1-15 hwriter_1.3.2
[13] KEGGREST_1.24.0 RCurl_1.95-4.12
[15] doParallel_1.0.14 cowplot_0.9.4
[17] RSQLite_2.1.1 europepmc_0.3
[19] bit_1.1-14 enrichplot_1.4.0
[21] xml2_1.3.2 httpuv_1.5.2
[23] assertthat_0.2.1 viridis_0.5.1
[25] xfun_0.7 hms_0.5.2
[27] evaluate_0.14 promises_1.0.1
[29] progress_1.2.2 caTools_1.17.1.2
[31] Rgraphviz_2.28.0 igraph_1.2.4.1
[33] DBI_1.0.0 geneplotter_1.62.0
[35] htmlwidgets_1.3 purrr_0.3.3
[37] backports_1.1.4 annotate_1.62.0
[39] biomaRt_2.40.0 vctrs_0.2.0
[41] withr_2.1.2 ggforce_0.2.2
[43] triebeard_0.3.0 prettyunits_1.0.2
[45] cluster_2.1.0 DOSE_3.10.1
[47] lazyeval_0.2.2 crayon_1.3.4
[49] labeling_0.3 pkgconfig_2.0.2
[51] tweenr_1.0.1 ProtGenerics_1.16.0
[53] nnet_7.3-12 rlang_0.4.5
[55] lifecycle_0.1.0 affyio_1.54.0
[57] rprojroot_1.3-2 polyclip_1.10-0
[59] graph_1.62.0 Matrix_1.2-18
[61] urltools_1.7.3 base64enc_0.1-3
[63] whisker_0.3-2 ggridges_0.5.1
[65] png_0.1-7 viridisLite_0.3.0
[67] bitops_1.0-6 R.oo_1.22.0
[69] KernSmooth_2.23-16 blob_1.1.1
[71] workflowr_1.4.0 mixsqp_0.1-97
[73] stringr_1.4.0 SQUAREM_2017.10-1
[75] qvalue_2.16.0 gridGraphics_0.4-1
[77] memoise_1.1.0 magrittr_1.5
[79] gdata_2.18.0 zlibbioc_1.30.0
[81] compiler_3.6.3 KEGGgraph_1.44.0
[83] htmlTable_1.13.1 Formula_1.2-3
[85] tidyselect_0.2.5 yaml_2.2.0
[87] GOSemSim_2.10.0 locfit_1.5-9.1
[89] latticeExtra_0.6-28 ggrepel_0.8.1
[91] fastmatch_1.1-0 tools_3.6.3
[93] rstudioapi_0.11 foreach_1.4.4
[95] foreign_0.8-75 git2r_0.26.1
[97] gridExtra_2.3 farver_1.1.0
[99] ggraph_1.0.2 digest_0.6.25
[101] rvcheck_0.1.3 BiocManager_1.30.10
[103] shiny_1.3.2 pscl_1.5.2
[105] later_0.8.0 httr_1.4.1
[107] colorspace_1.4-1 XML_3.98-1.20
[109] fs_1.3.1 truncnorm_1.0-8
[111] splines_3.6.3 ggplotify_0.0.3
[113] xtable_1.8-4 jsonlite_1.6.1
[115] UpSetR_1.4.0 zeallot_0.1.0
[117] R6_2.4.0 Hmisc_4.2-0
[119] pillar_1.4.2 htmltools_0.3.6
[121] mime_0.7 glue_1.4.0
[123] DESeq_1.36.0 interactiveDisplayBase_1.22.0
[125] codetools_0.2-16 lattice_0.20-38
[127] tibble_2.1.3 curl_4.3
[129] gtools_3.8.1 GO.db_3.8.2
[131] survival_3.1-8 rmarkdown_2.1
[133] munsell_0.5.0 DO.db_2.9
[135] GenomeInfoDbData_1.2.1 iterators_1.0.10
[137] reshape2_1.4.3 gtable_0.3.0